Finally, we want to compare the performance of the symbolic regression algorithm with other popular machine algorithms in literature, such as polynomial regression and artificial neural networks (ANN) for the same \(\sin (x)\)  case. Polynomial regression may be considered as one of the oldest machine learning algorithms [1]}, inspired by Legendre and Gauss's works, and implemented in a robust algorithm by Gregonne in 1815 [2]}. Polynomial regression is the most appropriate “traditional” regression method to arrive at polynomials such as the Maclaurin series. In polynomial regression, the structure of the fitting equation and the degree of the polynomial are predefined. For our case we will use two different polynomials one with a degree of \(n\)  = 3, and other one with \(n\)  = 7. We use the same training data set that we used for the symbolic regression, and for implementation, we will use the Polyfit function from the open-source Numpy library written in python [3]}. The main output of the algorithm is the coefficients of the following equation:
\(y(x) = a_0+a_1x+... a_nx^n\) 
